RLsite: Integrating 3D-CNN and BiLSTM for RNA-ligand binding site prediction

Abstract Accurate identification of RNA-ligand binding sites is essential for elucidating RNA function and advancing structure-based drug discovery. Here, we present RLsite, a novel deep learning framework that integrates energy-, structure- and sequence-based features to predict nucleotide-level binding sites with high accuracy. RLsite leverages energy-based three-dimensional representations, obtained from atomic probe interactions using a pre-trained ITScore-NL potential, and models their contextual features through a 3D convolutional neural network (3D-CNN) augmented with self-attention. In parallel, structure-based features, including network properties, Laplacian norm, and solvent-accessible surface area, together with sequence-based evolutionary constraint scores, are mapped along the RNA sequence and used as sequential descriptors. These descriptors are modeled using a bidirectional long short-term memory (BiLSTM) network enhanced with multi-head self-attention. By effectively fusing these complementary modalities, RLsite achieves robust and precise binding site prediction. Extensive evaluations across four diverse RNA-ligand benchmark datasets demonstrate that RLsite consistently outperforms state-of-the-art methods in terms of precision, recall, Matthews correlation coefficient (MCC), area under the curve (AUC), and overall robustness. Notably, on a particularly challenging test set composed of RNA structures containing junctions, RLsite surpasses the second-best method by 7.3% in precision, 3.4% in recall, 7.5% in MCC, and 10.8% in AUC, highlighting its potential as a powerful tool for RNA-targeted molecular design.